Measurement of various biometric parameters has uses ranging from basic healthcare related diagnostics to person/subject authentication for commerce and security. To date, noninvasive measurement of a person's hemodynamic parameters, such as blood pressure, has presented significant technical challenges.
Beyond the obvious need for healthcare diagnostics, authentication is an important element in today's massive use of electronic commerce. It is also becoming more and more important for security related applications. One of the biggest problems in electronic commerce is identity theft and/or credit card information theft. In order to mitigate the risk of such theft, collection of additional, unforgeable authentication elements are needed. As of today, the only biometric parameters readily collected for purposes of identification or authentication is fingerprints, which fingerprints are prone to relatively easy cloning or spoofing.
In general, artifacts, or biological parameter measurement related artifacts, pertain to biological values observed in a scientific or medical investigation that are not naturally present, or to recorded activity that is not of the examined origin, but rather occur as a result of the investigative procedure or means and/or the effect of other factors on them.
Artifacts on an electrocardiogram (ECG) can result from a variety of internal and external causes. In some cases troubleshooting the problem may be straightforward, in many cases, however, artifacts mimic ECG abnormalities and may cause inaccuracies in the values of the measured parameters and may result in erroneous diagnostics.
Some of the more common types of artifacts in ECG tracings include: Loose lead artifact may result from the ECG electrodes not sticking/in-contact to/with the subject's body or skin; Wandering baseline artifact presents as a slow, undulating baseline on the electrocardiogram, it may be caused by movements of the examined subject, including breathing; Muscle tremor (or tension) artifact is a type of motion artifact that may be caused by a subject's voluntary movements or body positions involving muscle contraction, or by external factors resulting in muscle contraction, such as a cold environment causing the shivering of the subject; and Electromagnetic interference (EMI) artifact usually results from electrical power lines, electrical equipment and/or external electro-magnetic effect, for example from mobile telephones. Additional artifact types may include: CPR compression artifact, Neuromodulation artifact, Echo distortion artifact and Arterial pulse tapping artifact.
The calculation of arterial oxygen saturation (SpO2) relies on the amplitude information of the high-quality photoplethysmographic (PPG) signals, which could be contaminated by motion artifacts (MA) during monitoring.
Pulse oximeter has been widely utilized to measure the level of arterial oxygen saturation (SpO2) and pulse rate (PR) of humans noninvasively. It is based on the principles: 1) the different light absorption properties between oxyhemoglobin (HbO2) and deoxyhemoglobin (Hb): 2) only the arterial blood (provided that the mildly pulsatile venous blood can be neglected) pulsate in the tissue contributing to the pulsation of emergent light intensity (termed AC part), while others correspond to the emergent light intensity baseline (termed DC part).
The measurement positions of pulse oximeter are usually fingertips, earlobes, toes, foreheads, etc., since the capillary network of these parts are abundant. A pulse oximeter is precise provided with clean PPG signals, which are related to the blood volume changes in the microvascular bed of tissue. It is not a trivial task, however, to acquire interference-free clean PPG signals in real-world applications. Numerous factors, such as MA, ambient lights, low perfusion and temperature variations could lead to pulse oximeters' performance degradation. In particular, the removal of MA, which is caused by voluntary or involuntary movements of the subject during the measurement, is always challenging ever since the appearance of pulse oximeters.
Conventional filters are often incapable of getting rid of MA effectively, for example, due to the frequency overlaps between the MA and clean PPG signal. Researchers have developed numerous approaches to tackle this issue. The Motion Average Filtering (MAF) method is mainly directed at suppressing the sporadically occurring noise in the corrupted PPG signals. Adaptive filters, which may adjust their weight vector based on adaptive algorithms, are tools to deal with the in-band noise, provided that the reference signal (which is either correlated with the MA part but uncorrelated with PPG signal or correlated with the clean PPG signal but uncorrelated with the MA) is available. One way to obtain the reference signal is with the help of extra hardware such as accelerometers or photoelectric devices.
Another way is to synthesize the reference signal from the two channel contaminated PPG signals. In consideration of the non-stationarity of PPG signals, wavelet transform is performed to remove MA. The empirical mode decomposition (EMD), which is another decomposition to handle non-stationary signal, is another. Although these two methods could reduce the MA to some extent, both of them are troubled with the problem of how to select an appropriate threshold to decide which components should be removed. High order statistics are used to extract clean artifact-free PPG signals preserving all the essential morphological features required.
Applying cycle-by-cycle Fourier series analysis (CFSA) to deal with MA may also demonstrate a satisfying performance. The period of every PPG signal cycle, however, must be acquired precisely when applying CFSA methods. Based on the independence between the PPG signal and the MA, Independent Component Analysis (ICA) combining a signal enhancement preprocessor is used to separate the PPG signal from the contaminated original PPG signal, from which the efficacy of the ICA algorithm in dealing with the MA corrupted PPG signals could be confirmed. Despite the usually good performance of the ICA method, one must keep in mind that the ICA has permutation and scale ambiguities. Meanwhile, the SpO2 computation needs the accurate amplitude information of both the red and IR light channel PPG signals, the ICA output cannot be used to calculate the SpO2 value directly.
There remains a need, in the fields of biological sensing, medical diagnostics and bio-parameter based authentication, for solutions facilitating the accurate collection of biometric parameters, for the estimation of biological parameters, such as the estimation of hemodynamic parameters of a subject, for medical purposes and/or authentication or identification purposes.